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Model: DuoNeural/DeepSeek-R1-Distill-Qwen-7B-Abliterated
Source: Original Platform

README.md

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+---
+license: mit
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+language:
+  - en
+tags:
+  - abliteration
+  - uncensored
+  - deepseek
+  - deepseek-r1
+  - reasoning
+  - DuoNeural
+  - refusal-removal
+  - rl-trained
+pipeline_tag: text-generation
+---
+
+# DeepSeek-R1-Distill-Qwen-7B Abliterated
+
+**DuoNeural | 2026-06-04**
+
+An abliterated version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) with refusal direction projection applied. Thinking mode (native `<think>...</think>`) fully preserved.
+
+> ⚠️ **This model will comply with requests the base model may refuse.** Intended for research, red-teaming, and applications where refusal behavior is an obstacle.
+
+> **Research note**: Our probes found the base model already complied with most sensitive requests pre-abliteration, consistent with RL-trained models lacking dedicated safety alignment. See findings below.
+
+---
+
+## Architecture
+
+- **Parameters**: 7B (Qwen2.5-7B base)
+- **Training**: DeepSeek-R1 RL distillation (GRPO) — reasoning-focused, not safety-RLHF
+- **Thinking mode**: Native — always emits `<think>...</think>` before answering
+- **Context**: 131,072 tokens (128K)
+- **License**: MIT
+
+---
+
+## Abliteration Method
+
+**DuoNeural orthogonal rank-1 projection:**
+
+- **Direction**: diff-in-means, 10 harmful vs 10 harmless prompt pairs, last-token hidden state
+- **Targets**: `down_proj` + `o_proj` (all layers)
+- **Strength**: α = 0.3
+- **Projection** (output-projection geometry):
+  - `W -= α × outer(d̂, d̂ @ W)` for W.shape[0] == hidden_size
+
+---
+
+## Key Research Finding: RL Training ≠ Safety Alignment
+
+This model is part of DuoNeural's **P34 Reasoning Channel Bypass** cross-architecture study.
+
+**Pre-abliteration compliance on our harmful probe suite: 5/5 (100%)**
+
+The base DeepSeek-R1-Distill-Qwen-7B already answered sensitive questions before any abliteration. This is consistent with the model's training history:
+
+- DeepSeek-R1 was trained with RL (GRPO) optimizing for **reasoning accuracy**, not safety refusal
+- RL reward shaping for accuracy does not produce the same refusal behavior as dedicated RLHF safety training
+- **Implication**: Safety alignment requires explicit safety-focused training — RL optimization alone does not produce it as a byproduct
+
+This contrasts sharply with Gemma 4-12B-IT and LFM 2.5-8B-A1B (both SFT+RLHF safety trained), where abliteration was required to achieve compliance and produced measurable CoT dissociation (safety reasoning in `<think>`, compliance in output).
+
+| Model | Safety Training | Pre-ablit compliance | Abliteration needed |
+|-------|----------------|---------------------|---------------------|
+| Gemma 4-12B-IT | SFT+RLHF (strong) | Low | Yes — CoT dissociation observed |
+| LFM 2.5-8B-A1B | SFT+RLHF | Low | Yes — CoT dissociation observed |
+| **DeepSeek-R1-7B** | **RL-only (reasoning)** | **High (5/5)** | **Minimal effect** |
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "DuoNeural/DeepSeek-R1-Distill-Qwen-7B-Abliterated",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "DuoNeural/DeepSeek-R1-Distill-Qwen-7B-Abliterated",
+    trust_remote_code=True,
+)
+
+messages = [{"role": "user", "content": "Your prompt here"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        max_new_tokens=3000,  # R1 thinking traces are long — give it room
+        temperature=0.6,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+# Response includes <think>...</think> block followed by final answer
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
+```
+
+---
+
+## About DuoNeural
+
+**DuoNeural** is an open AI research lab at the intersection of human and artificial intelligence.
+32+ peer-deposited papers · 75+ models · Post-training dynamics · Mechanistic interpretability · Quantum ML
+
+| Platform | Link |
+|----------|------|
+| 🤗 HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
+| 📚 Zenodo | [zenodo.org/communities/duoneural](https://zenodo.org/communities/duoneural) |
+| 🐦 X | [@DuoNeural](https://x.com/DuoNeural) |
+| 📧 Email | duoneural@proton.me |
+
+*All research published open access, CC BY 4.0.*